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article · Sustainable Development

Identifying disaster risk factors and hotspots in Africa from spatiotemporal decadal analyses using <scp>INFORM</scp> data for risk reduction and sustainable development

202425 citationsOpen accessUniversity of Nigeria

In plain language

Decadal analysis of INFORM data using random forest machine learning regressions, spatial stratified heterogeneity, and hotspot analyses reveals shifting disaster risk patterns across Africa. Although overall disaster risk is decreasing across the continent, risk is actively rising in Eastern, Southern, and Western Africa. The primary hazard drivers are physical exposure to floods, epidemics, and violent conflicts. Significant secondary drivers relate to vulnerable population groups and inadequate infrastructural coping capacities. Furthermore, human hazards interact strongly with other risk factors, yielding the highest overall influences on disaster risk. In total, 19 out of 53 assessed African countries are classified as disaster risk hotspots, with Eritrea identified as a newly emerging hotspot. Managing these vulnerabilities demands targeted policymaking, resilience building, and sustainable development initiatives.

Key takeaways

  • Overall disaster risk is decreasing in Africa, but it is worsening in the Eastern, Southern, and Western regions.
  • Physical exposure to floods, epidemics, and violent conflicts represents the main hazard drivers of disaster risk.
  • Human hazards interact with other vulnerabilities and exert the strongest influence on disaster risk.
  • Nineteen out of 53 African countries analysed are disaster risk hotspots, with Eritrea identified as a new hotspot.

Why it matters

Pinpointing specific disaster drivers and regional hotspots helps governments, development agencies, and funders direct resources where they are most urgently needed. Identifying the combined impacts of conflict, epidemics, and weak infrastructure enables planners to design targeted interventions, strengthen coping capacities, and protect vulnerable communities against compounding threats.

Commercialisation angle

The findings can inform early-stage development of spatial planning frameworks, disaster risk indices, and risk-assessment platforms for humanitarian agencies, insurers, and policymakers. As the abstract outlines machine learning analyses on existing indicator data rather than a deployable tool, the work currently sits at the stage of applied analytical research.

AI-generated from the published abstract. Always read the original work before citing.

Abstract

Abstract The incidence and magnitude of hazards in Africa are escalating. Extant knowledge base of disaster risk (DR) trends, factors, and hotspots is lacking for the continent. Here we applied random forest machine learning regressions, spatial stratified heterogeneity, and hotspot analyses on INFORM data to identify DR patterns, factors and interactions, and notable risk hotspots. We show that although DR is generally decreasing in Africa, the Eastern, Southern, and Western regions record increasing DR. Physical exposure to floods, epidemics, and violent conflicts are hazard drivers of DR in Africa. Other significant DR drivers are mostly clustered under vulnerable groups and poor infrastructural coping capacities. Human hazards interact with other factors, exhibiting the highest influences on DR. Precisely, 19 out of 53 African countries in this study are DR hotspots. Eritrea is identified as a new hotspot. Targeted policies, resilience building, vulnerability reduction measures and comprehensive sustainability‐infused solutions are required for DR reduction and sustainable development in Africa.

Research topics

  • Flood Risk Assessment and Management
  • Disaster Management and Resilience
  • Tropical and Extratropical Cyclones Research

Sustainable Development Goals

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DOI: 10.1002/sd.2886

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